Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data

Hailun Sun; Rongtao Jiang; Shile Qi; Katherine L. Narr; Benjamin SC Wade; Joel Upston; Randall Espinoza; Tom Jones; Vince D. Calhoun; Christopher C Abbott; Jing Sui

NeuroImage: Clinical (Jan 2020)

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data

Hailun Sun,
Rongtao Jiang,
Shile Qi,
Katherine L. Narr,
Benjamin SC Wade,
Joel Upston,
Randall Espinoza,
Tom Jones,
Vince D. Calhoun,
Christopher C Abbott,
Jing Sui

Affiliations

Hailun Sun: Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
Rongtao Jiang: Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
Shile Qi: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
Katherine L. Narr: Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
Benjamin SC Wade: Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
Joel Upston: Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
Randall Espinoza: Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
Tom Jones: Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
Vince D. Calhoun: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
Christopher C Abbott: Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA; Corresponding authors.
Jing Sui: Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China; Corresponding authors.

Journal volume & issue: Vol. 26

Abstract

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Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.

Published in NeuroImage: Clinical

ISSN: 2213-1582 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry: Neurology. Diseases of the nervous system
Website: http://www.journals.elsevier.com/neuroimage-clinical/

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